1048 results about "Completion time" patented technology

The invention relates to the technical field of construction progress management, in particular to a BIM (Building Information Modeling)-based construction progress analysis and regulation and control system, which comprises a component sequence identification module, a component time acquisition module, a sequence deviation identification module, a node offset analysis module and a plan time regulation and control module. According to the method, the three-dimensional coordinates of the BIM components are extracted to establish a spatial sorting relation, a priority system with spatial constraints is constructed, the sequence recognition precision is improved, time mapping is formed by combining state data and completion time, ordered association from the site progress to the model components is achieved, the completion time of the previous components is compared to recognize abnormal procedures, and quantitative records are formed. The actual offset and the planned floating time are fused to judge the offset type, the influence weight of the component in the progress network is quantified in combination with the offset direction and the sequence offset number, a comprehensive evaluation index is formed, the construction progress management has a clear logic structure, and the construction progress regulation and control precision and response efficiency are improved.

The invention discloses a multi-agent cooperation method based on a large language model, and relates to the field of agent cooperation control, and the method comprises the steps: constructing a multi-agent system; establishing a multi-agent coordination resource allocation model; a task allocation problem in the resource allocation model is converted into a multi-objective optimization problem, and optimization objectives comprise the minimum task completion time and the optimal resource utilization rate; and solving a multi-objective optimization problem by using an improved particle swarm optimization (PSO) to obtain an optimal cooperation scheme among the master control agent, the task creation agent, the intention arrangement agent and the at least one professional agent. The improved particle swarm optimization (PSO) evaluates the uncertainty of each candidate solution through multiple times of random sampling, and when the uncertainty exceeds a threshold value, cross validation is performed through a multi-agent voting mechanism. For uncertainty accumulation of a multi-agent system under a complex cognitive task, the efficiency and stability of collaborative decision and task scheduling of the multi-agent system are improved.

The invention discloses an AGV scheduling system optimization method based on dynamic priority adjustment and real-time load balance, and the method comprises the steps: dynamically monitoring the emergency degree and arrival time of cargos, enabling a system to adjust the task priority in real time, guaranteeing the priority processing of emergency cargos, and giving consideration to the scheduling efficiency of common cargos; information such as current positions, loads and electric quantity of the AGVs is collected, weighted load values are calculated, weight factors are dynamically adjusted according to task types, load balance is achieved, and the situation that part of the AGVs are overloaded and part of the AGVs are insufficient in load is avoided; and finally, scheduling optimization is carried out by comprehensively considering factors such as the adaptation degree between the task and the AGV, the path distance, the predicted completion time and the load balance, and the overall performance of the AGV system is fully exerted. The flexibility, efficiency and resource utilization rate of the AGV scheduling system are effectively improved, and the AGV scheduling system is suitable for various scenes such as logistics storage and production and manufacturing.

The invention discloses an intelligent engineering vehicle scheduling method based on artificial intelligence, and the method comprises the steps: constructing a scheduling objective function which at least comprises the engineering vehicle transportation cost, the task completion time cost, the path congestion cost and the energy consumption cost; establishing a multi-dimensional task demand prediction model based on historical task data and real-time traffic data, and outputting a task distribution prediction value and a traffic state prediction value in a future time period; inputting the model into an engineering vehicle dynamic scheduling model established based on a deep reinforcement learning algorithm for iterative optimization; and generating a real-time scheduling instruction based on the optimized dynamic scheduling model, dynamically allocating task paths and resources of the engineering vehicle, and monitoring an execution state in real time to adjust a scheduling strategy. According to the method, efficient, economical and environment-friendly intelligent scheduling of the engineering vehicle is realized through construction of a scheduling objective function, multi-dimensional demand prediction, deep reinforcement learning dynamic optimization and real-time scheduling and monitoring.

The invention provides a discrete MES-oriented intelligent production scheduling system, method and equipment and a medium, and belongs to the technical field of discrete manufacturing industry production scheduling. Data is acquired through a sensor and serves as production scheduling data; establishing a material inventory data association order ID and establishing an index; determining a process sequence constraint, a calculation equipment productivity constraint, a material supply constraint and an order priority constraint; initializing a population based on a genetic algorithm, randomly generating N groups of process sorting schemes, calculating a utilization rate index, and taking a comprehensive score as a fitness value; outputting a better solution set; the optimal solution of the genetic algorithm is used as initial pheromone distribution, high-quality path pheromones are enhanced according to the actual production effect of the completion scheme, and if the preset number of iterations is reached, the operation is stopped, and an optimized production scheduling scheme is output; and checking the production scheduling plan through a graphical interface. Through continuous optimization of the procedure sorting scheme, the equipment utilization rate is effectively improved, the total order completion time is shortened, the production resource configuration is optimized, and the production efficiency is improved.

The invention provides a resource scheduling control method and system for a big data server, and the method comprises the steps: constructing a multi-dimensional resource portrait module, collecting the CPU, memory, network, storage I / O load and task queue length of each node in real time, and predicting a resource demand trend through a time sequence algorithm; extracting characteristics such as calculation intensity, data dependence, memory requirements, network transmission quantity and the like; adjusting the weight coefficients of the resource utilization rate, the task completion time and the energy consumption efficiency according to the system load and the historical effect; establishing a bipartite graph model by taking a resource trend as a node feature and a task vector as an edge feature, and calculating a matching score through graph convolution and a multi-objective optimization function; the scheduling scheme is synchronized by adopting a consistency algorithm; automatic rollback and reallocation are carried out when resources are detected to be insufficient; and optimizing a weight coefficient and a network parameter through reinforcement learning. Through the method, the system resource utilization rate can be improved, the task execution efficiency is improved, the overall scheduling effect stability is improved, and the system fault recovery time is shortened.

The invention discloses an intelligent scheduling and path planning system and method for a mine carry-scraper, and belongs to the technical field of mining, and the system comprises an environment sensing module which is used for collecting the environment data of a mine operation area in real time, and the environment data comprise the equipment state, the road condition and the dynamic obstacle position; processing the collected data to generate environment state parameters; and the task scheduling module is used for allocating operation tasks according to the equipment state, the task target and the environment state parameters, and performing global optimization on task allocation to minimize task completion time and energy consumption. Generating a task allocation scheme based on the equipment state and the task target through a task scheduling module; the path planning module realizes optimal path planning through multi-device game optimization, and the global optimization control module coordinates the work of each module, optimizes global targets such as task completion time, energy consumption and conflicts, realizes deep fusion of scheduling and path planning, and improves the working efficiency, global optimality and robustness of mining devices.

The invention discloses an AI-based intelligent factory dynamic production scheduling optimization method and system, and belongs to the technical field of factory dynamic production scheduling, and the method comprises the following steps: obtaining and integrating the working states of various types of equipment in a factory, the stock position states of different materials, and the manual data; the production order state, the process route requirement, the plan and the completion time of each process and the delivery date requirement of the customer are determined; generating an initial production scheduling plan through an AI optimization algorithm in combination with supply chain material data, factory storage space, equipment switching cost, production rules, each order process dependency relationship and a production target; according to the method, the initial scheduling plan is generated by acquiring and integrating multiple types of production data, the dynamic events are monitored, multiple schemes are generated through evaluation, the optimal scheme is selected through economic model evaluation, and production refinement, dynamic response and benefit optimization are achieved.

The invention relates to an intelligent manufacturing management method and equipment based on a digital twinning technology, and a medium. The method comprises the following steps: constructing a collaborative model, collecting real-time data based on the collaborative model, establishing a manufacturing resource intelligent configuration simulation model, and generating an initial scheduling scheme; the initial scheme is optimized through a genetic algorithm, an executable scheduling scheme is generated, and the optimization target is to minimize the maximum completion time; applying the executable scheme to a workshop, monitoring a dynamic event and collecting data; when a dynamic event occurs, generating a rescheduling scheme based on an event-driven rescheduling strategy and Monte Carlo tree search; the rescheduling scheme is transmitted to a workshop, tasks are adjusted, and meanwhile process execution data are recorded; constructing a digital twinborn model based on recorded data, analyzing dynamic event influence, identifying key nodes in combination with a fault propagation network, and optimizing a dynamic scheduling strategy; and an optimized scheduling parameter and a model updating strategy are generated by using a digital twin simulation result, and closed-loop management and real-time response are realized.

The invention relates to the technical field of computing power sharing, in particular to a computing power sharing system and method in a multi-tenant environment, and the system comprises a computing power monitoring module, a load analysis and prediction module, a task priority judgment module, a resource optimization configuration module and a fault management module. According to the invention, by implementing real-time resource monitoring and data analysis and accurately capturing the speed and mode of resource consumption, the resource allocation is more accurate, the waste of computing power resources is avoided, the response capability is improved, the load prediction is generated by using historical and real-time data, the computing power resources can be ensured to be fully utilized in the peak period of demand, overload is avoided, and the service life of the system is prolonged. Through intelligent analysis of the resource utilization rate and the predicted completion time and optimization of priority ranking of the tasks, the emergency tasks can be rapidly responded, implementation of a real-time computing power distribution strategy is achieved, high-efficiency computing power resource use is guaranteed, the stability and reliability of the system are improved, and the cost problem caused by fault response delay is reduced.

A power system human resource allocation method based on multi-agent cooperation comprises the steps that S1, a human resource task collection module is constructed, and newly-added human tasks of a power system are collected in real time; s2, quantitatively decomposing each human task into a plurality of sub-tasks, calculating the complexity of each sub-task, and constructing a human task balance optimization model; s3, constructing an intelligent agent role allocation model according to the sub-task complexity, the intelligent agent capability matrix and the real-time load of the intelligent agent; and S4, performing iterative operation through a multi-objective optimization algorithm, solving the human task balance optimization model and the agent role allocation model, allocating the subtasks to the agents to obtain a task allocation matrix and estimated completion time, and executing the subtasks by the agents according to the allocation matrix to realize allocation of the human tasks. The design not only considers the balance of resource consumption and processing time of human tasks, but also allocates based on allocation efficiency and task completion efficiency.

The invention relates to an unmanned system cluster distributed task cooperative scheduling method and system. The system comprises a ground station general control scheduling module, an unmanned aerial vehicle cluster and a distributed monitoring and scheduling agent module. The method comprises the following steps: calculating the static priority of each task in a DAG task graph; tasks with high priorities are scheduled firstly; sequentially selecting a node with the minimum power consumption perception completion time EA-EFT from all the unmanned aerial vehicles, and allocating a task to the node; each monitoring scheduling node monitors the task execution progress in real time; and after the node fails, task allocation is stopped, the tasks are rescheduled according to the priorities of the tasks, the optimal allocation nodes of the tasks are recalculated based on an EA-EFT model, and a recommendation mechanism is triggered to take over the tasks of the failed nodes after each scheduling fails. The problems that an unmanned aerial vehicle cluster scheduling scheme is poor in adaptability, low in efficiency and incomplete in fault-tolerant mechanism are solved, and the scheduling efficiency, the system robustness and the energy efficiency of an unmanned aerial vehicle cluster in a complex task scene are improved.

The invention belongs to the field of workshop dynamic scheduling, and provides a dynamic disturbance-oriented tire vulcanization workshop intelligent flexible scheduling method and system, and the method comprises the steps: building a production scheduling model with the minimization of the maximum completion time as an optimization target; obtaining an initial processing sequence and a selectable machine set of each workpiece process, and solving by taking the production scheduling model as a target function to obtain an optimal initial scheduling scheme; when a dynamic event occurs in the production process, detecting the type of the dynamic event and the occurrence time of the event, and extracting a completed process, a processing process and an unstarted process; and determining a to-be-scheduled process based on the influence of the dynamic event on the processing process and the unstarted process, retaining the process processing sequence of the optimal initial scheduling scheme, updating the optimal processing machine of the to-be-scheduled process, and generating a rescheduling scheme. The device only replaces a machine in an unprocessed process, is embedded into a cooling time buffer area between the same workpiece processes, solves the problem of insufficient cooling of the rubber material, and guarantees the quality and process stability.

The invention discloses an HCPS workshop system-oriented scheduling method based on multi-agent deep reinforcement learning, and the method comprises the steps: taking the minimization of completion time as a target, and constructing a target optimization model; independently establishing a worker efficiency fluctuation model for each worker; the method comprises the following steps: modeling an HCPS workshop system as a Markov decision process, and designing decision points; designing a state space; and designing an action space: designing a reward function based on an ARSI training mechanism. According to the method, the multi-agent deep reinforcement learning is introduced, so that the accuracy and efficiency of workshop scheduling are remarkably improved, and particularly, the production process is effectively optimized by independently modeling each worker and considering factors such as fatigue and skills of the worker.

The invention discloses a multi-agent distributed task collaboration method and system, and relates to the technical field of computers, and the method comprises the steps: broadcasting a to-be-allocated task set to each agent, enabling the agents to estimate the task expected completion duration of each to-be-allocated task, and generating a corresponding to-be-allocated task expected duration matrix; receiving a to-be-allocated task expected duration matrix and a historical allocation task estimated duration from each agent; constructing a constraint optimization model according to the to-be-allocated task expected duration matrix corresponding to each agent and the historical allocation task estimated duration; and solving the constraint optimization model through a global constraint optimization algorithm, and allocating each task to be allocated to a corresponding agent according to an optimal solution. Therefore, the flexibility of task allocation is improved, and resource waste and unreasonable allocation in task allocation are reduced.

The invention discloses a flexible job shop scheduling method based on deep reinforcement learning, and belongs to the field of intelligent manufacturing and intelligent scheduling, and the method comprises the steps: firstly, taking the minimization of the maximum job completion time and the maximization of the average machine utilization rate as optimization targets, and obtaining a target model; then representing a flexible job shop scheduling problem by using a disjunction graph model to obtain a scheduling state; according to the scheduling state and the target model, an end-to-end learning framework is constructed, and the framework is combined with a multi-attention network for deep feature extraction and a decision network for extensible decision, and a Markov decision process is utilized to obtain a job scheduling model; and finally, the multiple attention network constructs a production adaptability operation-AGV-machine scheme by using the complex relationship among the operation, the AGV and the machine to support the decision of the decision network, and obtains a scheduling schedule by using the job scheduling model to complete flexible job shop scheduling. According to the method, complex modes and relations in data can be better captured, and the performance and generalization ability of the model are improved.

The invention relates to a clustering and entropy-guided reentrant hybrid flow shop scheduling method. The method comprises the following steps: step 1, establishing a problem model; step 2, setting algorithm operation parameters; 3, adopting an initialization strategy to generate an exploration population and a development population; 4, judging whether a first-stage termination condition is met or not, if not, executing a first-stage evolutionary strategy and an updating strategy on the exploration population and the development population, and otherwise, executing the step 5; 5, constructing an elite population; 6, judging whether a second-stage termination condition is met or not, and if not, executing a second-stage evolutionary strategy on the elite population; otherwise, outputting a Pareto solution set; and 7, updating the elite population. According to the method, dynamic balance of global exploration and local development is realized, and the solution distribution can be improved while the solution set convergence is ensured, so that the completion time and the total energy consumption are reduced, the production cost is reduced, and the workshop scheduling efficiency is improved.

The embodiment of the invention discloses a flexible job shop scheduling method based on preference-driven graph reinforcement learning, and relates to the field of shop dynamic scheduling in an intelligent manufacturing technology. According to the method, by constructing a multi-objective optimization model, multiple objectives of the job shop can be optimized at the same time, including the minimum completion time, the total delay and the total cost. Wherein an imperfect maintenance model is constructed, and the maintenance demand and the maintenance opportunity of each machine are dynamically determined. And capturing a complex relationship between the operation and the machine by using an improved graph neural network. In combination with a preference-driven mechanism, a maintenance plan and workshop scheduling are adjusted in real time through a graph reinforcement learning method, and efficient priority scheduling rules under different preferences are learned, so that an integrated decision of machine allocation, an operation sequence and maintenance arrangement in dynamic scheduling is realized.

The invention relates to the technical field of security auditing, in particular to a security auditing method and system for an industrial control system, and the method comprises the following steps: aiming at a key control task, a communication task and a security task of a real-time operating system, collecting a period, a starting timestamp, a finishing timestamp, a central processing unit occupied time slice and peak memory usage amount data. According to the method, the period, timestamp, central processing unit occupation and memory usage data of a key task of a real-time operating system are collected, a task execution time boundary and a resource consumption envelope are set, and then an expected relation rule set of a task time sequence and resource consumption is constructed by applying historical data statistics and logic rule deduction; and meanwhile, the key length of symmetric and asymmetric encryption, initialization vector generation, hash algorithm selection, key derivation parameters and encryption operation context are stipulated, so that a comprehensive and specific ICS behavior specification baseline is established.

The invention belongs to the technical field of AGVs, and provides an AGV transportation method and a robot, and the method comprises the steps: obtaining a task list and the initial position of each AGV; the task execution sequence is optimized through tabu search so as to minimize the time consumption of final task completion, the average completion time and the weighted cost of the number of pauses; according to the optimization sequence, constructing a space-time diagram of node representation time and space positions for each AGV; planning the path of each AGV on the space-time diagram by adopting a time enhancement algorithm, regarding the paths of other AGVs as time obstacles, and adopting a path cost function; in the path planning process, the AGVs are allowed to wait in situ to avoid collision, and the paths of the AGVs are updated in real time; performing Bezier curve smoothing on the planned discrete path for three times to generate a continuous and controllable driving track; and finally, each AGV is controlled to execute the task along the track. According to the invention, the problems of low task scheduling efficiency and frequent path conflicts of a plurality of AGVs in the prior art can be solved.

The invention discloses a port tallying operation management method and system, and the method comprises the steps: dividing port tallying operation scenes, and defining and quantifying the node features of each operation node in each operation scene; constructing a dynamic monitoring strategy model, inputting the operation node features into the dynamic monitoring strategy model, and obtaining a monitoring strategy matched with the operation nodes; constructing a port tallying operation directed graph, and training and generating a node completion time window prediction model based on the constructed port tallying operation directed graph; obtaining operation data of each current operation node according to a dynamic monitoring strategy, inputting a node completion time window prediction model, and obtaining prediction completion time of each operation node; and for each operation node, predicting completion time, comparing preset time corresponding to each operation node, and performing operation node progress early warning according to a comparison result. According to the application, precise intelligent monitoring and early warning of port tallying operation can be realized, and the management quality is effectively improved.

The invention provides a processing and assembling combined production workshop scheduling optimization method considering dynamic disturbance, and the method comprises the steps: building a combined scheduling model: dividing a product production process into a part processing process, an assembling process and an assembling subsequent process, defining the correlation constraint of processing and assembling, and carrying out the processing and assembling operation; comprising part machining sequence consistency, part machining completion requirements before assembly, buffer area capacity limitation and a dynamic disturbance event response mechanism; establishing a multi-objective optimization function: taking minimization of maximum completion time, equipment idle time, processing energy consumption cost and tool switching times as optimization objectives, and generating a scheduling model in combination with process time dependence, machine selection uniqueness and tool switching time constraint; dynamic disturbance processing: when an equipment fault occurs or a new order is inserted, updating an available machine state or combining tasks to be processed, and triggering rescheduling to generate a new scheme; an improved MOEA / D algorithm is used for solving; and outputting and applying a scheduling scheme: selecting a final scheme for production guidance according to the non-dominated solution set.

The invention discloses a hybrid heterogeneous cloud workflow scheduling method based on reinforcement learning, and belongs to the technical field of cloud computing. The method comprises the following steps: aiming at a cloud workflow scheduling problem, by taking minimization of completion time as a target and taking cost and resources as constraints, a three-dimensional collaborative constraint model is constructed, and the cost constraints comprise server-free function budget and virtual machine budget; integrating the hyper-heuristic framework into a reinforcement learning algorithm; an improved reinforcement learning algorithm is adopted to solve the cloud workflow scheduling problem, optimal execution resources are selected, and an optimal scheduling scheme is obtained; and performing real-time scheduling according to the optimal scheduling scheme, and introducing a deviation feedback mechanism to monitor an execution error in real time. According to the method, the workflow scheduling problem is decomposed into a closed-loop optimization process of state perception and action decision, dynamic environment perception, multi-target tradeoff and online strategy optimization are deeply fused, the global search function is achieved, local optimization can be achieved, the algorithm complexity is low, and robustness is high.

The invention discloses a production process scheduling method, system, equipment and medium based on the industrial Internet of Things, and relates to the technical field of industrial Internet of Things data processing, and the method comprises the steps: obtaining a to-be-scheduled production process, obtaining a preset production queue which is composed of a current production task and a plurality of to-be-processed production tasks, obtaining completion time and production information; obtaining environment trend information, obtaining a dynamic correction index, obtaining a process utilization rate, and obtaining a process utilization index based on the correction model, the process utilization rate and the dynamic correction index; obtaining inventory unit time cost and delivery deadline, and obtaining a scheduling index based on the scheduling model, the completion time, the process utilization index, the inventory unit time cost and the delivery deadline; and sequentially arranging the plurality of production tasks to be processed according to the scheduling indexes from small to large, generating a scheduling queue, and executing the production tasks according to the scheduling queue. The method has the advantages of dynamic environment self-adaption, multi-target collaboration and good scheduling effect.

The invention relates to the technical field of engineering construction progress intelligent control, in particular to an engineering construction progress intelligent control method and system based on digital twinning, and the method comprises the steps: obtaining a preset plan logic relation set; acquiring a real-time process state set of the construction site; determining a plan failure index through real-time logic deviation calculation; in response to the plan failure index being greater than a preset failure determination threshold, determining that the plan fails and switching to a logic emergence mode; in response to the plan failure index being less than or equal to the failure determination threshold, maintaining the plan driving mode; deducing and generating an emergence logic set through emergence logic confidence calculation; determining a failure logic set; reconstructing to generate a hybrid logic model; the prediction completion time is deduced again; outputting a decision support signal; according to the method, the defect that a traditional system cannot judge the failure is overcome, the prediction accuracy is improved, and invalid warning out of reality is avoided.

The invention provides a deadline constraint-based DAG application dynamic scheduling optimization method and system, and the method comprises the steps: converting an application deadline into an instant reward of each task scheduling through employing a DAG structured encoder, a Transform encoding network based on gating feature fusion, and a multi-action selection deep reinforcement learning task scheduling method based on a pointer network, in combination with a dynamic mask scheme, the mobility of a DAG application and the dynamic nature of edge resources are dealt with, then priority subtask selection and real-time decision of the scheduling position of the priority subtask selection are made, and the completion time and execution energy consumption of the application are reduced. In order to stabilize and accelerate DRL scheduler training, task encoder training and reinforcement learning training are decoupled, and a DAG encoder is pre-trained based on self-supervised learning.

The invention relates to a dual-resource constraint flexible job shop scheduling method for reducing worker load, which comprises the following steps: S1, construction of a worker load model: dividing the worker load model into four conditions of light work, moderate work, micro-severe work and rest according to daily work arrangement of workers, and setting the maximum working time length for each worker, the calculation module is used for calculating extra workloads; in the scheduling method provided by the invention, three different initialization strategies are combined, and the proportion of the initialization strategies in a population is set, so that the diversity and quality of an initial solution are ensured, specifically, the used initialization strategies comprise random initialization, initialization according to the process completion time and initialization according to the process remaining time; under the random initialization strategy, the initial solution of the population has great diversity, which is helpful for avoiding the trouble of a local optimal solution.

The invention discloses a manufacturing task autonomous negotiation and execution method based on a large language model agent, and the method comprises the steps: constructing a production scheduling agent, an equipment management agent, a material distribution agent and a quality control agent, analyzing a natural language task instruction through the production scheduling agent, and decomposing the natural language task instruction into subtasks; each agent calculates a utility value based on the load rate, the resource matching degree, the estimated completion time and the historical success rate, and performs structured negotiation to achieve a task allocation consensus; a prediction-check-rollback architecture is adopted to generate an action instruction sequence, the sequence is compiled into a time Petri network transition sequence, and reachability verification is carried out based on hard security constraints; production environment data is collected in real time to trigger anomaly detection and re-negotiation, and a formalized security verification and causal anti-factual reasoning parameter updating mechanism is introduced. According to the method, unstructured instruction understanding, autonomous task planning, multi-agent collaborative decision and closed-loop optimization are realized, and the problems of real-time performance, safety and interpretability of a large language model in manufacturing control are solved.

The invention relates to the technical field of distributed flow shop scheduling, and belongs to a solving method for a distributed heterogeneous replacement flow shop scheduling problem. Comprising the following steps: determining to take minimization of total weighting completion time as a solving target, initializing a population, and optimizing the population to obtain an optimized population; selecting a solution from the initial population by using a ternary tournament method, and executing six local search operators in sequence, and executing each local search operator for five times; replacing the solution with the maximum target value in the optimized population with a new solution to obtain a new population, if no improvement is continuously carried out for five times, triggering a disturbance mechanism to update the new population, combining the new population after disturbance with the new population before disturbance, sorting each solution in the combined population from small to large according to the target value of the solution, and selecting part to continue iteration; and continuing to execute the next round of search until the limited maximum time is reached, and outputting the solution with the minimum target value of the solution. The method has the positive effects of improving the production efficiency and the stability of the production line.

The invention discloses a full-link-based production traceability management system and method, and relates to the technical field of data management, and the method comprises the steps: calling a historical traceability record for tracing each production link of a product, obtaining a target value of the traceability record, and extracting a target record in the traceability record according to the target value; detecting the product characteristics of the product corresponding to the target record to obtain the difference degree of each product characteristic, and further extracting the target characteristics in the product; establishing a characteristic function of a corresponding characteristic value changing along with time, and obtaining the characteristic duration of the product; and obtaining the tagging time corresponding to the to-be-detected traceability record and the input completion time of the traceability code, obtaining an early warning value of the to-be-detected traceability record, and carrying out early warning prompt. By analyzing the historical traceability record and combining the product characteristics of the traceability product, whether the current to-be-detected traceability record is reasonable or not is obtained, and early warning prompt is performed, so that the reliability of the traceability system is improved.